Remaining Useful Life Prediction of Cutting Tools Based on Support Vector Regression

2019 International Conference on Advances in Materials, Mechanical and Manufacturing

材料科学;机械制造

Liu, Y.C.^1 ; Hu, X.F.^1 ; Sun, S.X.^1

Shanghai Key Laboratory of Advanced Manufacturing Environment, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China^1

关键词: Accurate prediction;    Computer numerical control;    Condition based maintenance;    Remaining useful life predictions;    Second phase;    Signal features;    Signal length;    Support vector regression (SVR);   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/576/1/012021/pdf
DOI  :  10.1088/1757-899X/576/1/012021

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Remaining useful life (RUL) prediction of cutting tools is critical to effective condition based maintenance for reducing downtime, ensuring quality and avoiding accidents. In this paper, a RUL prognostic method based on support vector regression (SVR) is proposed for predicting cutting tool's life. The proposed method consists of two main phases: an off-line phase and an on-line phase. In the first phase, the signal features are extracted from raw data, and then the SVR models with considering different length of signals at past times are established to reflect the relationship between monitoring data and tool life. In the second phase, the constructed models are used to predict cutting tool's RUL, and the best signal length for accurate prediction result is obtained. The proposed method is applied on experimental data taken from a computer numerical control (CNC) rotor slot machine in a factory. The result shows the validity and practicability of this method.